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This study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Vietnam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost outperformed linear regression. Furthermore, for random forest, XGBoost, and AdaBoost, the coefficients of determination were higher, indicating a better fit to the data. Moreover, time series forecasting models such as autoregressive integrated moving average, seasonal autoregressive integrated moving average, and exponential smoothing were used to predict future energy consumption and carbon dioxide emissions in Vietnam. The models were trained and verified using historical data. The time series model findings showed that energy consumption rose steadily during the predicted timeframe. The autoregressive integrated moving average model predicted 162258.77 ktoe of energy use by 2050, whereas the seasonal autoregressive integrated moving average and exponential smoothing modes predicted 160673.8 ktoe and 153206.44 ktoe of energy use, respectively. By 2050, the autoregressive integrated moving average model anticipated 6.51 metric tons of carbon dioxide emissions per capita, the SARIMA model 7.769 metric tons, and the exponential smoothing model 6.22 metric tons. The findings show how machine learning techniques and time series models may be used to estimate energy usage and carbon dioxide emissions in Vietnam. These insights could assist Vietnam government in making informed judgments concerning energy planning and policy development. [ABSTRACT FROM AUTHOR]

Continued improvements in living standards and the economic well-being in the megacities have led to a huge surge in vehicular density. The worst environmental outcome of the same has been persistent unsafe urban air quality, thanks to vehicular emission. Further, the existing inspection and maintenance programs, conceived to check such emission remain largely ineffective, particularly in developing countries. This is due to the absence of a thorough assessment of the vehicle's compliance with the in-use emission norms generated through reliable field investigation data. To address this gap, the present comprehensive study collected real-time tailpipe emission data from 2040 cars in Delhi, India. Exhaust emission parameters, namely, CO (carbon monoxide), HC (hydrocarbon), and SE (smoke emission), were recorded from both petrol and diesel-driven cars of private ownership, in collaboration with the emission compliance test centers. The performance of cars was assessed in terms of their compliance with the in-use BS (Bharat Stage) emission norms. The one-of-its-kind study reported the petrol cars to be highly compliant toward the BS IV norm while faring even better toward BS II for both CO and HC emissions (80–90%). The conformance to the HC norm was found to be typically better than that for CO (85–90% versus 75–80%). For the diesel-driven cars, BS III compliance levels were reported relatively better compared to BS IV (90% in the case of the former against 80% in the latter's case). Further, the study puts forward a clear indication that the in-use emission norm and maintenance status of cars have a direct and negative relationship with tailpipe emission parameters. Cars of both overseas and domestic origin have almost equal degrees of compliance with the emission norms (over 80% in any case). The study recommends the incorporation of these two critical vehicular variables, i.e., maintenance status and in-use emission standard in the emission certification policy. [ABSTRACT FROM AUTHOR]

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As part of the global efforts to make aviation activities more environmentally friendly, the worldwide goal is to achieve a 50% reduction in the 2005 emissions by 2050. In this context, aviation emissions represent a critical challenge to aviation activities, especially with the increasing travel demand up to the beginning of the COVID-19 crisis, starting in 2020. One of the potential drivers that would help the aviation industry reduce its emissions is the use of sustainable aviation fuel (SAF). In this study, we analyzed the impact of SAF from an air traffic flow management (ATFM) perspective, considering delay and re-routing costs. We developed an optimization model that considers, in addition to the traditional ATFM costs, fuel costs and carbon dioxide emissions. We investigated the impact of accounting for these two new aspects, that is, fuel costs and emissions, on ATFM performance, and we compared SAF with conventional fuel. The analysis of a real case study revealed that, in addition to delay and re-routing costs, fuel cost should be included in the ATFM model so that the resulting solution becomes economically and environmentally realistic for airlines. The increase in the fuel cost and network delays when using SAF requires setting an appropriate carbon price under an emission policy, such as the carbon offsetting and reduction scheme for international flights policy, to make SAF more attractive. Furthermore, flexible re-routing programs for flights operated using SAF make it advantageous from an ATFM perspective. [Display omitted] • We develop a green air traffic flow management model. • The model is tested on the Swedish airspace. • We analyze the impact of sustainable aviation fuel on network delays and costs. • We calculate the carbon price to compensate for the increase in fuel and ATFM costs. • We investigate the trade-off between ATFM costs and emissions. [ABSTRACT FROM AUTHOR]

Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres.

Environmental conditions strained the existing transportation network to replace its conventional gasoline-based engines by electric vehicles (EVs) to reduce its share in pollution. EVs promise a potential solution, but they fail to motivate end users because of their inherent drawbacks. Utilisation of EVs in vehicle-to-grid (V2G) environment is expected to boost EVs penetration but will burden the conventional power generations for their energy demand. For improvement of the environmental conditions, it is likely that the authorities will come up with a strict emission control policy. This study demonstrates the effects of emission policy on the V2G environment. The proposed problem is modelled as a multi-objective optimisation problem. Non-dominated-sorting-based genetic algorithm (NSGA-II) is used to solve this problem. This study also demonstrates a long-term impact analysis of a system consisting of a V2G market, end users and EVs by considering time variance in EVs penetration and vehicles' performance degradation. This analysis can help in planning and formulating the future V2G market.

Abstract Greenhouse gas emission (GHGE) taxes on food products have recently been proposed as means to help reduce agricultural emissions. Numerous authors have calculated potential GHGE reductions in case such a tax was implemented in certain countries or regions. They did however assume a reduced production of GHGE-intense foods equal to the decline in demand induced by the tax. This omits however possible increases of net-exports that might offset such a demand reduction. Herein, the market dynamic behind this so-called "emission leakage" is explained and its effect quantified for a greenhouse gas tax in the European Union. We use the European Forest and Agricultural Sector Optimization Model for the quantitative analysis and simulate a greenhouse gas tax on all food products, based on their individual emission levels. The partial equilibrium model covers all world regions and hence the tax's effects on international trade of agricultural commodities can be examined. It was found that 43% of the greenhouse gas reduction indicted by a domestic consumption reduction is lost through emission leakage. This already includes the mitigating effects of a production shift from inefficient to efficient producers that is another consequence of increased exports from the European Union. A greenhouse gas emission tax on food products is hence much less efficient than previously proposed, if it is not introduced globally or trade is not restricted. Graphical abstract Image 1 Highlights • The effect of a GHG tax on food products in the EU is quantified. • We explain and visualize the general market dynamics of carbon leakage. • Unlike authors before us, we quantify carbon leakage effects. • Previous studies vastly overestimate the effects of a the tax, for omitting leakage. • 43% of the mitigation is lost to leakage, despite a shift to efficient producers. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) emissions from community-based consumption are a major contributor to global greenhouse gas emissions. However, little is understood about how demographic, socioeconomic, and household factors may contribute to community CO 2 emissions variation. Using traditional regression methods to model community emissions does not discriminate which of these factors are responsible for CO 2 emissions in high carbon emitting communities versus low carbon emitting communities, leading to policy development that does not consider emission variation across communities. To address this issue, we used quantile regression to model these effects on different quantiles of community emissions for 1451 communities across Canada and each province in 2015, respectively. The results showed that population, followed by affluence, were the most important variables affecting total community emissions, while affluence was the most important factor affecting per capita community emissions. However, the effect sizes were not consistent across quantiles, decreasing for population and increasing for affluence from low to high emission communities. Population density was significant across all communities except the lowest quantile communities, with the effect size increasing from smaller to larger communities. Additionally, our measure of poverty was significantly associated with increases in total and per capita emissions for all quantiles at the national level. Our finding that factors responsible for CO 2 emissions varied across communities of different quantiles suggests that successful emission reduction policies must account for the diversity of community characteristics, particularly considering variation in population and affluence. Our study also shows poverty alleviation is an effective means for CO 2 emission reduction and should be considered when adopting emission reduction policies. • We used quantile regression to model the variation in community CO 2 emissions. • Factors responsible for CO 2 varied greatly from low to high CO 2 emission quantiles. • Population and affluence affected CO 2 most but were inconsistent across quantiles. • Emission reduction policies must account for these community emission variations. • Poverty alleviation could have the co-benefit of limiting community CO 2 emissions. [ABSTRACT FROM AUTHOR]

Spatially separated locations may differ greatly with respect to their electricity demand, available space, and local weather conditions. Thus, the regions that are best suited to operating wind turbines are often not those where electricity is demanded the most. Optimally, renewable generation facilities are constructed where the maximum generation can be expected. With transmission lines limited in capacity though, it might be economically rational to install renewable power sources in geographically less favourable locations. In this paper, a stochastic bilevel optimisation is developed as a mixed-integer linear programme to find the socially optimal investment decisions for generation expansion in a multi-node system with transmission constraints under an emissions reduction policy. The geographic heterogeneity is captured by using differently skewed distributions as a basis for scenario generation for wind speeds as well as different opportunities to install generation facilities at each node. The results reinforce that binding transmission constraints can greatly decrease total economic and emissions efficiency, implying additional incentives to enhance transmission capacity between the optimal supplier locations and large demand centres. [ABSTRACT FROM AUTHOR]

Under risk of abrupt climate change, the occurrence hazard is added to the social discount rate. As a result, the social discount rate (1) increases and (2) turns endogenous to the global warming policy. The second effect bears profound policy implications that are magnified by economic growth. In particular, we find that greenhouse gases (GHG) emission should be terminated at a finite time so that the ensuing occurrence risk will vanish in the long run. Due to the public bad nature of the catastrophic risk, the second effect is ignored in a competitive allocation and unregulated economic growth will give rise to excessive emissions. In the long term, the GHG emission paths under the optimal and competitive growth regimes lie at the extreme ends of the range of feasible emissions. [ABSTRACT FROM AUTHOR]

A market experiment was designed to empirically investigate the potential effectiveness of a governmental subsidy system to reduce sales and therefore production of environmentally harmful products. The important issue of whether the subsidy system preserves competitiveness was also examined. In the experiment two levels of a subsidy for unsold units were compared with no subsidy. To simulate the way in which subsidy levels may vary across time in real markets, the effects of high and low uncertainty regarding the subsidy level were also investigated. The results showed that subsidies, whether known and fixed or uncertain and varying, did not erode competition but nevertheless led to higher prices, which resulted in fewer sales. In the control condition a price war resulting in decreasing prices and increasing sales were observed. Several ways in which the proposed subsidy system may be implemented in the transport sector and other sectors are discussed. It is suggested that subsidies may make the adjustment process toward sustainable production less costly for the regulated parties. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) has consistently been linked to ozone (O 3) and secondary organic aerosol (SOA) formation, and ongoing emission policies are primarily focusing on total VOCs without addressing the association between regulation measures and secondary pollution characteristic. For enhancing VOCs emission policy, we investigated potential formation of O 3 and SOA based on analyses of node-specific VOCs concentration and species distribution in solvent-consuming industry. Although aromatics were found to contribute most to O 3 and SOA formation averagely (2.57 ± 2.14 g O 3 /g VOCs, 1.91 ± 1.67 g SOA/g VOCs), however, large disparity concerning emission and secondary pollution profile were identified among different emission nodes which demonstrated that regulation policy should be formulated based on comprehensive pollution characteristic. Therefore, emission nodes were classified into four clusters through data normalization, formatting and classification process, including aromatics dominated (7 emission nodes), aromatics-alkene dominated (4 emission nodes), aromatics-alcohols dominated (4 emission nodes) and alcohols dominated (4 emission nodes). And different dominating VOCs species were further obtained in each cluster. Subsequently, focusing regulation measures of reducing O 3 and SOA for different emission source clusters were proposed to guide pollution prevention and enhance future VOCs emission policies. Potential formation of O 3 and secondary organic aerosol were obtained based on analytical result of node-specific VOCs concentration and species distribution, and large disparity was identified among different emission nodes. Accordingly, the emission sources were classified into four clusters which could be regulated respectively. Image 1 • VOCs emission profile varied considerably in different emission sources. • Aromatics contributed to O 3 and SOA formation maximally, and alkene followed. • Four clusters were classified to propose focusing regulation measures and emission policy. [ABSTRACT FROM AUTHOR]